The Stability of Money Demand: Evidence from Turkey

The stability of money demand: Evidence from Turkey

PhD Chaido Dritsaki

Technological Institute of Western Macedonia, Greece

Department of Financial Applications

e-mail: [email protected]

PhD Melina Dritsaki

Brunel University

West London

e-mail: [email protected]

Abstract

Demand for money is an important macroeconomic relationship. Its stability has implications for the choice of monetary policy targets. The current study examines the stability of money demand function in Turkey from January 1989 to

May 2010. More specifically, it estimates the demand for narrow money in Turkey and evaluates its robustness and stability. Considering the economic reforms and financial crises in Turkey, it is found that there exists a well-determined instability for money demand and its dynamics which is adequately captured by cointegration and error correction models. Finally, the conclusions from the estimation of the impulse response functions show that interest rate causes the largest shift in money demand as well as in the industrial production.

Keywords: Demand for money, Monetary policy, Cointegration, Vector Error

Correction Model, Stability, Impulse response function. JEL: C22, C32, E41, E52

1. Introduction

Demand for money investigates what motivates people to hold money balances. Deducing from the estimations of money demand equations, the monetary authority can decide which monetary policies are better to implement under the current economic conditions. A stable demand function for money has long been perceived as a prerequisite for the use of monetary aggregates in the conduct of policy

(Goldfeld and Sichel, 1990).

The effectiveness and success of a monetary programme crucially depends on a stable money demand function. The stable money demand function ensures that the money supply would have predictable impacts on other economic variables such as inflation, interest rates, national income, and private investments. Therefore, the stability issue in money demand function becomes an interesting research area for researchers to test the effectiveness of a given monetary programme (Halicioglu and

Ugur, 2005).

Turkey has historically suffered a number of economic problems. Inflation was high and persistent, the use of foreign currency, was widespread and the banking sector was an oligopoly and domestic financial markets were shallow and volatile.

These problems led to a financial crisis in 2001. This crisis provided the impetus for a number of economic reforms and the central bank independence was increased.

An authoritative and independent Monetary Policy Committee was created concomitant with the increased role given to monetary policy. The fixed exchange rate was abandoned and monetary policy adopted an inflation target. These reforms were followed by a dramatic drop in the inflation rate (Butkiewicz and Ozdogan

2008).

2 Turkey, as a developing country, has undertaken a number of stability programmes since 1970s. Under the ongoing stability programme, the Central Bank of Turkey (CBT) has been following a base-money targeting strategy. Τhe necessary condition for effective monetary aggregate targeting is the existence of a stable long- run and short-run relationship between the monetary aggregate and the decreasing of inflation.

The issue of stability on money demand has been examined thoroughly in the last two decades using econometric methodologies, allowing scientists to have a more in-depth examination on the stability concept which happens to be a long term phenomenon.

The objective of this paper is to estimate a long-run version of money demand in Turkey through cointegration tests and over the period 1989:M1 until 2010:M5.

To achieve this objective, the paper is organised as follows. Economic theory for money demand is discussed in section 2. Section 3 provides a brief review of the literature on stability of the money demand functions for Turkey. Section 4 deals with data analysis. Section 5 illustrates the methodology used. Section 6 provides the results. Finally, the concluding remarks are contained in section 7.

2. Economic theory on money demand

In the classical theory money was held for transaction purposes or as a medium of exchange. Money supply is defined as the sum of notes and coins, and the demand deposits. The quantity theory of money is based on the assumptions that money supply is exogenous and the income velocity of money is stable. If the velocity is stable, then the demand for money is stable. Hence, there is a tight link between the amount of money and the level of nominal income. In addition, this theory postulates

3 that the economy moves to a long run full-employment equilibrium. In the long run, the price level depends upon the quantity of money in the economy.

Monetarist approach to the quantity theory also assumes that the money supply is exogenous and income velocity of money is stable. However, this approach differs from the conventional quantity theory in explaining the link between the money supply and the level of income. Monetarists postulate a direct transmission mechanism from monetary to real sector through the real balance effect.

Friedman's quantity theory postulates that in the long run the income elasticity of money demand is unity and velocity of money is constant; whereas Tobin's transactions demand theory postulates that the income elasticity is 0.5 and velocity is not constant. The theories on the value of long run income elasticity of money demand are totally different as a result of their postulates about the money demand.

The main argument against conventional quantity theory is that the transaction demand for money does not only have a response to the transaction needs, but also has a response to the physiological factors (such as expectations), institutional factors

(such as credit facilities), and to any stochastic shock in the economy (Aysu, Insel

1997).

Keynes (1936) developed the liquidity preference theory which explicitly highlights the transaction, precautionary and speculative motives for holding money.

In the Keynesian approach there is a link between the quantity of money and the level of income in an economy, but this approach does not postulate that economy moves to a long run full-employment equilibrium. It is assumed that the interest rate has an important effect on the money demand and the income velocity of money is not stable. Keynesians claim that there is an indirect transmission mechanism that works through the interest rate effect on investment, and through the multiplier effect on the

4 real sector of the economy. In a theoretical framework if (i) the economy does not tend to move to a long run full employment level; or (ii) the income velocity of money is not stable and does not depend upon the rate of interest; or (iii) there is not a causality from money supply to either income or the price level, then it would be possible to assume that money supply is exogenous. The changes in the postulates such that instable velocity, financial innovations, expectations, and preferences lead to the rejection of the quantity theory and result in the theory of an endogenous money supply. The theory of endogeneity of money supply is based on political rather than economic behaviour. That is, if there is an increase in demand for money, the central bank cannot control the money supply, but can control the interest rate. Thus the purpose of monetary policy is to target the rate of interest not the money supply. The quantity of money is determined by the demand for it. Therefore, the rate of interest has no effect on the amount of money individuals wish to hold, rather has an indirect effect on demand due to changes in the level of income (Aysu Insel 1997).

Friedman (1956) opposed the Keynesian view that money does not matter and presented the quantity theory as a theory of money demand. He modelled money as abstract purchasing power (meaning that people hold it with the intention of using it for upcoming purchases of goods and services) integrated in an asset and transactions theory of money demand set within the context of neoclassical consumer and producer behaviour microeconomic theory. Friedman argued that the velocity of money is highly predictable and that the demand for money function is highly stable and insensitive to interest rates. This implies that the quantity of money demanded can be predicted accurately by the money demand function (Kumar, Webber and Fargher

2010).

5 3. Empirical studies in Turkey

This section includes the literature review on the empirical studies for Turkish economy to modelling money demand.

Metin (1994) estimates M1 narrow money demand for the period 1948:Q1-

1987:Q4. The results estimated confirm the existence of a long run money demand relationship with relatively high positive income elasticity and also a negative inflation elasticity as opportunity cost for the money demand equation.

In another study, Kogar (1995) tries to test whether there exists a stable long run money demand function for Turkey and Israel, which experience high inflation during the period under investigation. For the Turkish case, using quarterly data in the period 1978:Q1- 1990:Q4, it is found that there exists a long run relationship between real money (M1 and M2) demand, real income, inflation and exchange rate with an elasticity of income slightly lower than unity and also an elasticity of exchange rate significantly low.

Similarly, Mutluer and Barlas (2002) analyse broad money demand in Turkey between 1987 and 2001, a period characterized by a process of financial sector liberalization, implemented using various structural reforms and deregulations. Their results indicate the existence of a long run relationship for real broad money in

Turkey, with a unitary income elasticity estimated. Also, the results show that, both exchange rate and inflation rate have substantial impact on the Turkish broad money demand.

Furthermore, Akıncı (2003) investigates the relationship between real money balances, real income, and the opportunity cost variables in Turkey using quarterly data for the period 1987Q1-2003Q3. The estimated results indicate that there exists a

6 long run relationship between real currency issued, private consumption expenditure as scale variable, interest rates on government securities and the exchange rate.

The empirical relationship between money, real income, interest rates and expected exchange rate, is modelled by Civcir (2003) who also examines the constancy of this relationship, especially in the light of financial reform, deregulation of financial markets and financial crises in Turkey. The results obtained indicate the existence of a stable real broad money demand relationship with a positive unitary income elasticity confirming the quantity theory of money and negative opportunity cost variables. The results also reveal that the demand for money in Turkey is stable, despite the economic reforms and financial crises. Altınkemer (2004) investigates the existence of a stable long-run money base demand and gives some implications for

Turkey’s implicit inflation targeting policy.

Halicioglu and Ugur (2005) analyse the stability of the narrow money demand function (M1) in Turkey for the period 1950 - 2002. They estimate and test for the stability of Turkish M1 by cointegration procedure proposed by Pesaran et al. (2001) alongside CUSUM and CUSUMSQ stability tests. They demonstrate that there is a stable money demand function and it could be used as an intermediate target of monetary policy in Turkey.

Saatcoglu and Korap (2005) try to examine the money demand and its determinants for the period 1987:Q1-2004:Q2. They construct an empirical money demand model for Turkish economy, and compare the estimated results with the findings of some other empirical money demand studies carried out on Turkish economy.

Saatcoglu, el al (2006) investigate whether the money multiplier process in the

Turkish economy is stable and can be forecasted. Research results show that the

7 processes which convert the base money supply into the final monetary aggregates are unstable and decrease the effectiveness of monetary policies pursued by the Central

Bank of the Republic of Turkey (CBRT).

Akcaolayan, and Dommez (2008) attempt to test the stability of money demand relations for four money aggregates in Turkey. They use the Johansen multivariate cointegration analysis covering the period 1990:Μ1-2005:Μ12. The findings indicate that there is a stationary long-run relationship between the different monetary aggregates, real income, domestic interest rates, foreign interest rates, and the real effective exchange rate.

Finally, Ozdemir and Saygili (2010) analyze the parameter constancy of the long run money demand function in Turkey. They argue that the conventional money demand function is augmented by using proxies for macroeconomic uncertainty. As a part of this methodology, they constructed a wide variety of measures that are believed to be related with our definition of uncertainty, which is interpreted as the loss of investors’ confidence regarding the well functioning of the economy. The variables that are calculated for this purpose are the budget deficit to GDP ratio, the variability of inflation, real output, basket and the Istanbul Stock Market Index at different frequencies. Their results suggest that money balances, income and interest spread are not cointegrated when the VAR system is missing a measure of economic uncertainty. Thus, they find stable long run relations and coefficients when the correct measures of uncertainty are introduced to the system.

4. Data Analysis

It is necessary that all the empirical models should start by considering the data generating process for the time series. This process should be able to generate all the statistical properties of the series conditional on past information.

8 In this analysis the monetary aggregate the real M1, (currency in circulation plus demand deposits), is taken as a proxy for the relevant measures of money. We use M1 as a proxy for the demand for money because the central bank is able to control this aggregate more accurately than broader aggregates such as M2 and M3.

The choice of M1 is also relevant for Turkey because the central bank has been targeting base money rather than broad monetary aggregates such as M2 and M3.

We also use the consumer price index, industrial production, and nominal interest rate. Logarithm values were used for money supply, consumer price index, industrial production, and nominal interest rate. All the data we use are from IMF

(International Monetary Fund) over the period 1989:1–2010:5. In this section we analyse data-series by looking at the relevant figures of correlation and covariance.

(a) Figures:

Figures of the series give visual information about the data generating process. In

Figure.1 it can be inferred that M1, consumer price index, and industrial production have upward trends; nominal interest rate are above their means for the period

1994Μ2 to 1994Μ5, and 2000Μ12 to 2001Μ2. From 2001:3 onwards, nominal interest rates have a downward trend.

Figure 1

Looking at Figure 1, there is a very sharp decrease in output from 2008:7 to

2009:2. Nominal interest rate is at its maximum in 1994:3, but also in 2001:2.

Consumer price index is at its minimum from 1989:1 until 1991:3, but maximum in

2010:4. In the case of consumer price index there is a steady increase from 1989:1 onwards. From the figures it can be seen that all the variables included in the model are affected by the 1994 and 2001 economic crises in Turkey. Accordingly, it

9 becomes inevitable to test the stability of the money demand equations in this analysis.

Figures of the levels of the real M1 variables, consumer price index, industrial production, and nominal interest rate, appear to have non-constant mean, and give some information about the non-stationarity. Instead, the figures of first differences show no evidence of changing means.

(b) Correlation and Covariance:

The correlation and covariance matrices clarify the direction and the degree of the relationships between variables in the system. Table.1 and Table.2 show the correlation and covariance matrices for the system variables.

Table.1a

Table.1b

The correlation coefficients for the level of the variables are very high, but not quite high for the first differences. Real M1 is highly and positively correlated with consumer price index and industrial production. This implies that there is a short-run homogeneity between M1 money and consumer price index and industrial production.

Thus an increase in the rate of growth of M1 money is expected to boost consumer price index and industrial production. Real M1 has a negative correlation with nominal interest rate.

Covariance matrix, providing the information about the direction of the relationship between variables, shows that almost all of the covariations are overhead.

This means that covariations between variables are related.

Table.2a

Table.2b

10 Table.2a and Table.2b reveal that all of the covariations are overhead. On this basis, it may be inferred that the variables in the model are highly related to each other. For example, the covariation between a change in the real M1 money and the rate of consumer price index is 48.47. This implies that a high inflation increases real cash balances. Figure 2 presents the consumer price index and nominal interest rate.

Figure 2

As shown in Figure 2, before 2002, nominal interest is always above the consumer price index, but after 2002 it is the consumer price index above the nominal interest. Thus changes in the nominal interest could sustain an equal hedge against consumer price index during the whole sample period.

5. Model and Econometric Methodology

Modelling the real money demand in Turkey, requires careful consideration not only for the choice of the relevant variables but also for the formulation of the model. Price homogeneity is imposed to define the demand for money in real terms.

We assume that there is no money illusion. Hence, the real money demand is defined to be an increasing function of industrial production and a decreasing function of opportunity costs, i.e. nominal interest rate and inflation. The nominal interest rate and consumer price index are considered to be the opportunity cost for money.

In Turkey, consumer price index is above the nominal interest rate after the year 2002 (Figure 2). A high consumer price index reduces the real cash balances and the relative attractiveness of financial assets. Thus, economic agents move towards the speculative motive and move away from cash that buys real assets or moving to dollarization in order to find good hedge against inflation. This is the main reason why inflation rate is included in the model.

11 Following Shigeyuki Hamori, Naoko Hamori (2008), the model includes money supply, price index, output and nominal interest rate, which can be written as:

M t  L(Yt , Rt ) LY  0 LR  0 (1) Pt where

Mt represents nominal money supply for period t;

Pt represents the price index for period t; (the consumer price index (CPI)

Yt represents industrial production for period t; and

Rt represents the nominal interest rate for period t.

Increases in industrial production bring increases in money demand ( LY  0 ) and

increases in interest rates bring decreases in money demand ( LR  0 ).

Getting the log of Equation (1) we get the following function:

ln(M t )  ln(Pt )   0  1 ln(Yt )   2 ln(Rt )  ut 1  0  2  0 (2)

The problem confronting the estimation of the function for money demand is that money demand, industrial production, price index, and interest rate can all be characterized as non-stationary I(1) variables. As such, each variable can be explained without any tendency to return to a long-run level. However, the theory expressed in the equation asserts that there exists a linear combination of these non-stationary variables that is stationary.

Unit root test

The Augmented Dickey–Fuller (ADF) (1979,1981) and Phillips-Perron (1988) tests were used to determine the presence of unit roots in the data sets. The ADF test is based on the estimate of the following regression:

k X t  0  1t  2 X t 1  iX t i  ui (3) i1

12 where, Δ is the first-difference operator, Xt is the natural logarithm of the series, δ0, δ1,

δ2, and αi are being estimated and ut is the error term. The null and the alternative hypothesis for the existence of unit root in variable Xt is: H0:δ2=0 against Hε:δ2<0. The

PP unit root test is utilized in this case in preference to ADF unit root tests for the following reasons. The PP tests do not require an assumption of homoscedasticity of the error term (Phillips, 1987) and the test corrects the serial correlation and autoregressive heteroscedasticity of the error terms.

Vector autoregressive estimation

A vector autoregressive model (VAR) describes the evolution of a set of k variables (called endogenous variables) over the same sample period (t =1,..., T) as a linear function of their p lags and possibly of their additional exogenous variables.

A VAR allows the data to determine the precise model specification and treats all variables as endogenous. Thus, a general polynomial distributed lag framework or

VAR (p) model can be written as follows: yt = c + A1yt-1 + A2yt-2 +…+Apyt-p + et (4) where yt is a k × 1 vector of endogenous variables. c is a k × 1 vector of constants.

Ai is a k × k matrix (for every i = 1, ..., p) and et is a k × 1 vector of error terms

For the vector of error terms we get:

E(et )  0 (every error term has mean zero).

E(etet)   (the covariance matrix of error terms is Ω (a k × k positive definite matrix).

13 E(etetk )  0 (for any non-zero k, there is no correlation across time. In other words there is no serial correlation in individual error terms).

Co-integration tests

The presence of long run equilibrium relationship between dependent and independent variables is referred to as cointegration. The two common tests for cointegration are the procedure of Engle and Granger (1987) and the procedure of

Johansen and Juselius (Johansen and Juselius, 1990, Johansen, 1992).

Following Johansen procedure, let us consider a VAR(p) model adapted to the

VEC representation:

p1 Yt  Yt 1  iYt i  X t  et (5) i1

Granger’s representation theorem asserts that if the coefficient matrix Π has reduced

Rank r < k then there exist k r matrices  and  each with rank r such that

   and Yt is I(0), r is the number of cointegrating relations (the cointegrating rank) which we identify using the trace statistics. Moreover, each column of  is a cointegrating vector. The elements of  represent the adjustment parameters in the

VECM. i capture the short-run effects of the time series. Johansen’s method consists of estimating the matrix Π from an unrestricted VAR with a maximum likelihood technique and testing whether we can reject the restrictions implied by the reduced rank of Π.

Order of integration of the variables

We should note that all variables used, have to be of the same order of integration. So we have the following cases:

1) All the variables are I(0) (stationary): in this case in a VAR model the variables are in their levels

14 2) All the variables are integrated I(d) (non-stationary) with d>0:

2a) The variables are cointegrated: the error correction term has to be included in the VAR. The model becomes a Vector Error Correction Model (VECM) which can be characterised as a restricted VAR model. Thus, the VAR (p) model can be written as: (Engle and Granger 1987).

Δyt = c + Π1Δyt-1 + Π2Δyt-2 +…+ΠpΔyt-p + biECt-1 + et (6) where

ECt-1 is the error correction term. bi is a k × 1 vector.

2b) The variables are not cointegrated: the variables have first to be differenced d times and one has a VAR in difference. Thus, the VAR (p) model can be written as follows:

Δyt = c + Γ1Δyt-1 + Γ2Δyt-2 +…+ΓpΔyt-p + et (7)

Vector error correction models

The variables are associated with the VAR approach at the cointegration level, before we can form the VECM. So, we need to ensure that the variables are cointegrated. There are other considerations where more than one cointegrating vector exists, thus we can theoretically have more than one error correction term.

The dynamic relationship includes the lagged value of the residual from the cointegrating regression, besides the first differences of variables that appear in the long-run relationship. The inclusion of the variables from the long-run relationship can capture short run dynamics. It is essential to test whether the long-term relationship established in the model gives the short-run disturbances. Thus, a dynamic error correction model, which forecasts the short-run behavior, is estimated on the basis of cointegration relationship. For this reason the residual with one-time

15 lag derived from the cointegration vector, is being incorporated into the general error correction model (ECM) (Jayasooriya 2010).

Granger Causality tests

Engle and Granger (1987) determine the duality between cointegration and the vector error correction models (VECM). Furthermore, they show that the application of causality should be done by the VECM since it is the ideal instrument for causality examination because among other things, it determines the speed of convergence of the relevant variables to their equilibrium.

Since we found evidence of cointegration, there must be either unidirectional or bidirectional Granger causality, because at least one of the error correction terms should be significantly different from zero by the definition of cointegration. The

VECM approach, apart from showing the direction of Granger-causality among the variables, enables to distinguish between ‘short-run’ and ‘long-run’ Granger causality.

Testing Stability of the Demand for Money

Testing for stability of money demand is important as money supply is one of the key instruments of monetary policy. If money demand is stable then money supply is the most suitable monetary policy instrument but if money demand function is not stable, central bank should use interest rate as the most appropriate instrument for the conduct of monetary policy. It is argued that evolution and development of the financial market together with innovation in information technology brings in element of sensitivity in the demand for broad money in the economy. (Singh and Pandey

2009). For estimating the money demand function, we have used the conventional methods for the test of stability of the money demand function, these tests include

CUSUM, CUSUMSQ and recursive estimation technique.

Impulse response function

16 The impulse response functions can be used to produce the time path of the dependent variables in the VAR, to make shocks from the explanatory variables. The impulse response function defines the effect that a random impulse shock has upon the endogenous variables of VAR model. Usually, these shocks are expressed using the standard deviations of the disturbance terms (one or two standard deviations).

Thus, the impulse response function describes the implications on the endogenous variables in a VAR model for a number of future periods when disturbance terms are volatile. VAR models are considered suitable for the achievement of satisfying predictions due to their structure and also to the capability of the estimation of impulse response function and the variance decomposition.

6. Empirical Results

The current paper investigates a long term dynamic relationship between demand for money and consumer price index, industrial production, and the nominal interest rate in the case of Turkey from 1989:M1 to 2010:M5 by using a dynamic model. The empirical model specification applies a unit-root test, cointegration, vector error correction model and the impulse response function. The results of the dynamic model are presented in the following section

Unit root test

Based on the unit root test as depicted in table 3, all unit root tests yield remarkably similar results for all variables namely LMP1, LY and LR, which are non stationary in their levels I(0) but become stationary in their first differences I(1).

Thus, we conclude that all series are I(1) at the 1% level of significance.

Table.3

Johansen Cointegration Test

17 Johansen and Juselius’s (1990) cointegration method was used for the cointegration analysis. The order of lag-length was determined by the Schwarz

Information Criterion (SIC) and the Akaike Information Criterion (AIC). Johansen and Juselius, procedure test results are presented in table 4.

Table.4

The test statistics reject the null hypothesis of no cointegrating relation at the 5

% significance level, hence there is a cointegration vector (see the trace test and the maximal-eigenvalue statistics for cointegration test in table 4). This indicates that there is a long run relationship between money demand, industrial production, consumer price index, and nominal interest rate over the sample period under investigation.

The next step is to report the Granger causality test results obtained by the vector auto regression (VAR). Since a cointegration vector exists, we run the Granger test with error correction terms from the cointegrating equations included in a regression with the variables in their first differences (ΔLMP1, ΔLY and ΔLR). (See equation 6 for the error correction model.) Results are reported in table 5.

Table.5

Results from table 5 indicate a short-term causality relationship between industrial production and demand for money, directed from the former to the latter.

Also we found a causal relationship from the nominal interest rate towards industrial production and finally a short-term causal relationship between money demand and nominal interest rate directed from money demand to interest rate. The same causal relationships have also been depicted in the long run.

Testing Stability of the Demand for Money

18 Figures 3 and 4 present the plot of CUSUM and Cumulative sum of squares at the 5% level of significance. The plots of the CUSUM and CUSUMQ show instability of the demand for money function during the period 1989:M1-2010:M5.

Figure 3

Figure 4

The results of the CUSUM and CUSUMQ of Square are no surprising for the case of the Turkish economy. Τhis instability in the demand for money may be due to the fact that during this time Turkish economy has undergone political uncertainties.

(Turkish economy has been through two crises and various reformations).

The plot of recursive residuals clearly shows any presence of instability as it reaches the upper band at around 2006 and the lower band after 1991 (see figure 5).

Other than these periods demand for money has been stable in Turkey.

Figure 5

Impulse response function

We begin our empirical investigation by testing for the order of the VAR using several information criteria. Based on the overall results of these tests reported in table 4, the optimal lag length was determined to be 2. The VARs are estimated using monthly data from January 1989 through Μay 2010. The estimated VAR coefficients are not very interesting themselves and thus omitted. Instead, we focus on the impulse responses and the variance decompositions.

Figure 6 plots the impulse responses of LMP1, LY, LR over a horizon of 36 months. Standard errors are calculated by the Monte Carlo method, with 1,000 repetitions (of ± 2 standard deviations).

Figure 6

19 Impulse responses suggest that shocks in LMP1 have a negative impact on the variable itself, there is also a small positive trend in the case of LY for the whole period whereas we observe a drop of LR for the fist 15 months followed by a stabilisation.

Shocks in LY cause a slight fall in LMP1 for the first month followed by a small constant increase. Also, for LY itself we observe a continuous declining trend for the whole period (apart from the 2nd and 3rd months). LP decreases in the first 15 months followed by stabilisation.

Shocks in LR cause a slight decrease on LMP1 in the first month followed by stabilisation. LY is stable throughout the whole period under investigation. Finally,

LR shows a decreasing trend for the whole period (apart from the 2nd and 3rd months).

The results from variance decompositions are reported in figure 7, and table

6a, table 6b and table 6c.

Figure 7

From Figure 7 we observe that the highest percentage error variance of demand for money (LMP1) is due to interest rate (LR) by 60%, by 40% from the demand for money (LMP1), and from the industrial production (LY) by 2%.

Similarly, the highest percentage error variance of industrial production (LY) is originating from the industrial production (LY) by 40%, from the interest rate (LR) by 38% and by 5% from the demand for money (LMP1). Finally, the highest percentage error variance of interest rate (LR) is originating from the interest rate

(LR) by 90%, from the demand for money (LMP1) by 10% and by 2% from the industrial production (LY).

The forecast error variance decomposition indicates the proportion of the movements in a sequence due to its “own” shocks versus shocks to the other

20 variables. The overall results of this exercise are reported in table 6a, table 6b and table 6c.

Table.6a

The results from variance decompositions suggest that, over a 36-month horizon, 21.78 percent of the forecast error variance of industrial production (LY) can be accounted by shocks of money demand (LMP1).

Table.6b

The results from variance decompositions suggest that, over a 36-month horizon, 75.54 percent of the forecast error variance of interest rate (LR) can be accounted by shocks to money demand (LMP1).

Table.6c

Table 6c indicates that demand for money (LMP1) explains nearly 46 percent of its forecast error variance.

7. Conclusions

A basic principle of monetary theory is that the monetary authority can control the monetary aggregates and forecast their growth paths. Under these conditions, monetary policy can check whether demand for money is stable and whether there exists any long-run relationship between demand for money and its determinants.

In this article, we attempt to examine the stability of money demand function in the case of Turkey for 1989:M1-2010:M5. The period under investigation is characterized by high inflation, financial liberalization, capital account liberalization, and financial innovation driven mainly by an increasing government debt (see Irfan

Civcir 2003). Besides the stability of money demand, we investigate the potential long-run relationship between demand for money and its determinants. Using cointegration Johansen techniques, we are able to demonstrate that there is a long-run

21 relationship between the demand money aggregate and its determinants: industrial production, consumer price index and nominal interest rate.

Results from the error-correction model revealed a short-run and a long-run causal relationship between industrial production and demand for money directed from the former to the latter. Similarly, with nominal interest rates and industrial production with the short and long-run relationship been directed from the nominal interest rate towards industrial production. Finally, a short and long-run relationship was detected between demand for money and nominal interest rate directed from the former to the latter.

We have also tried to incorporate the short and long-run dynamics of money demand function in order to perform a more robust account of the stability of money demand function. To this end, we utilized the CUSUM and CUSUMSQ stability tests.

The results show that the process that extends the basic money supply to the final monetary aggregates is unstable, decreasing the effectiveness of monetary policies implemented by the Central Bank of the Republic of Turkey (CBRT).

Finally, with the impulse response function estimation, using a recent alternative approach by Pesaran and Sin 1998 (known as the generalised impulse response analysis based on variance decompositions), we predicted our model variables. The findings from the impulse response function estimation are consistent with the view that shocks in interest rates cause the greatest variance in the demand for money (60%) followed by industrial production (38%).

The latest financial crises of November 2000 and February 2001 showed that it is very important to implement serious monetary programmes because money demand regulations alongside interest policies play a very important role in the

Turkish financial system. Hence, they should be carefully regulated in order to retain

22 stable and low levels of inflation. Thus, monetary policy gained importance after the aforementioned crises and the governments tried to use the policies according to the

Monetarist’s rules (i.e. rule-based policies).

The findings of the current study suggest that demand for money can be used as a target of monetary policy in Turkey. As far as the Turkish central bank’s monetary policy is concerned, we propose that stability of a money demand function will reduce the uncertainty associated with the financial system and will increase the credibility for a stable and developmental environment.

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Figure 1: Series in levels and first differences

M1 P 120000 160

100000 120 80000

60000 80

40000 40 20000

0 0 90 92 94 96 98 00 02 04 06 08 90 92 94 96 98 00 02 04 06 08

Y R 130 500

110 400

100 300 90

80 200 70

60 100 50

40 0 90 92 94 96 98 00 02 04 06 08 90 92 94 96 98 00 02 04 06 08

26 DM1 DP 28000 4 24000 20000 3 16000 2 12000 8000 1 4000

0 0 -4000 -8000 -1 90 92 94 96 98 00 02 04 06 08 90 92 94 96 98 00 02 04 06 08

DY DR 20 400

300 10 200

0 100 0

-10 -100

-200 -20 -300

-30 -400 90 92 94 96 98 00 02 04 06 08 90 92 94 96 98 00 02 04 06 08

Figure 2: Nominal interest and consumer price index 500

0 90 92 94 96 98 00 02 04 06 08

27 Table.1a: Correlation Matrix (Levels) Μ1 P Y R Μ1 P 0.929885 Y 0.840009 0.894402 R -0.529041 -0.566693 -0.507635

Table.1b: Correlation Matrix (First Differences) ΔΜ1 ΔP ΔY ΔR ΔΜ1 ΔP -0.027420 ΔY 0.194547 0.029538 ΔR 0.031309 -0.076714 -0.006545

Table.2a: Covariance Matrix (Levels) Μ1 P Y R Μ1 P 1528578.2 Y 532883.02 929.031 R -742844.51 -1302.88 -450.578

Table.2b: Covariance Matrix (First Differences) ΔΜ1 ΔP ΔY ΔR ΔΜ1 ΔP 48.4730 ΔY 2920.93 0.15380 ΔR 2662.52 -2.26250 -1.63956

Table.3: ADF and PP unit root tests ADF PP Level Difference Level Difference LMP1 -0.962 (1) -20.67 (0)*** -1.197 [4] -20.61 [3]*** LY -0.914 (13) -4.215 (12)*** -2.394 [23] -72.06 [79]*** LR -1.098 (1) -24.85 (0)*** -1.528 [9] -30.53 [13]*** Notes:

1. *** denote significant at 1% level of significance. 2. The numbers within parentheses followed by ADF statistics represents the lag length of the dependent variable used to obtain white noise residuals. 3. The lag lengths for ADF equation were selected using Akaike Information Criterion (AIC). 4. Mackinnon (1991) critical value for rejection of hypothesis of unit root applied. 5. The numbers within brackets followed by PP statistics represent the bandwidth selected based on Newey West (1994) method using Bartlett Kernel. 6. LMP1=ln(M1)-ln(P). 7. Exogenous:Constant.

28 Table.4: Johansen and Juselius’s Cointegration Test Results Null 5% critical value Hypothesis Trace test Max-Eigen Trace test Max-Eigen LMP1, LY, LR (Order VAR = 2) r = 0 45.31 33.79 29.79 21.13 r ≤ 1 11.52 11.41 15.49 14.26 r ≤ 2 0.108 0.108 3.84 3.84 Notes: 1. Critical values derive from Osterwald – Lenum (1992). 2. r denotes the number of cointegrated vectors 3. Akaike and Schwarz criterion are used for the order of VAR model

Table.5: Multivariate Granger causality results F-test T-test ΔLMP1 ΔLY ΔLR ECM ΔLMP1 2.14* 1.47 -4.68332*** ΔLY 1.17 1.87* -2.36179*** ΔLR 2.32* 0.23 -2.12932** Note: ***, ** and * indicate significance at the 1%, 5% and 10% levels of significance, respectively. Δ is the first different.

Figure 3. Cumulative Sums of Recursive Residuals 60

-60 90 92 94 96 98 00 02 04 06 08

CUSUM 5% Significance

Figure 4. Cumulative Sums of Squares of Recursive Residuals

29 1.2

-0.2 90 92 94 96 98 00 02 04 06 08

CUSUM of Squares 5% Significance

Figure 5. Recursive Residual Plots .6

-.6 90 92 94 96 98 00 02 04 06 08

Recursive Residuals ± 2 S.E.

Figure 6: Response to Generalized One S.D. Innovations ± 2 S.E.

30 Response to Generalized One S.D. Innovations ± 2 S.E.

Response of LMP1 to LMP1 Response of LMP1 to LY Response of LMP1 to LR .10 .10 .10

.05 .05 .05

.00 .00 .00

-.05 -.05 -.05

-.10 -.10 -.10

-.15 -.15 -.15 5 10 15 20 25 30 35 5 10 15 20 25 30 35 5 10 15 20 25 30 35

Response of LY to LM P1 Response of LY to LY Response of LY to LR

.08 .08 .08

.04 .04 .04

.00 .00 .00

-.04 -.04 -.04

5 10 15 20 25 30 35 5 10 15 20 25 30 35 5 10 15 20 25 30 35

Response of LR to LM P1 Response of LR to LY Response of LR to LR .3 .3 .3

.2 .2 .2

.1 .1 .1

.0 .0 .0

-.1 -.1 -.1

-.2 -.2 -.2 5 10 15 20 25 30 35 5 10 15 20 25 30 35 5 10 15 20 25 30 35

31 Figure 7: Variance Decomposition

Variance Decomposition ± 2 S.E.

Percent LMP1 variance due to LMP1 Percent LMP1 variance due to LY Percent LMP1 variance due to LR 120 120 120

100 100 100

80 80 80

60 60 60

40 40 40

20 20 20

-20 -20 -20 5 10 15 20 25 30 35 5 10 15 20 25 30 35 5 10 15 20 25 30 35

Percent LY variance due to LMP1 Percent LY variance due to LY Percent LY variance due to LR 120 120 120

100 100 100

80 80 80

60 60 60

40 40 40

20 20 20

-20 -20 -20 5 10 15 20 25 30 35 5 10 15 20 25 30 35 5 10 15 20 25 30 35

Percent LR variance due to LMP1 Percent LR variance due to LY Percent LR variance due to LR 120 120 120

100 100 100

80 80 80

60 60 60

40 40 40

20 20 20

-20 -20 -20 5 10 15 20 25 30 35 5 10 15 20 25 30 35 5 10 15 20 25 30 35

Table.6a: Generalized Forecast Error Variance Decomposition of LY Variance Decomposition of LY Months LMP1 LY LR S.E 6 0.430 94.204 5.364 0.1205 (1.016) (3.476) (3.353) 12 1.515 81.793 16.690 0.1489 (2.282) (8.437) (8.212) 24 4.824 59.957 35.217 0.1879 (5.233) (13.874) (12.864) 36 7.625 46.975 45.399 0.2178 (7.376) (15.786) (14.221)

Table.6b: Generalized Forecast Error Variance Decomposition of LR

32 Variance Decomposition of LR Months LMP1 LY LR S.E 6 6.228 0.683 93.088 0.4227 (3.878) (1.663) (3.989) 12 8.588 1.293 90.118 0.5302 (5.672) (3.267) (5.715) 24 11.611 2.200 86.188 0.6619 (8.472) (6.027) (8.543) 36 13.301 2.726 83.972 0.7554 (10.113) (7.731) (10.292)

Table.6c: Generalized Forecast Error Variance Decomposition of LMP1 Variance Decomposition of LMP1 Months LMP1 LY LR S.E 6 83.479 0.449 16.071 0.1743 (5.317) (1.117) (5.286) 12 63.738 0.828 35.433 0.2505 (9.992) (2.540) (9.797) 24 44.188 1.668 54.143 0.3687 (13.081) (5.199) (12.547) 36 36.350 2.324 61.324 0.4552 (13.973) (7.157) (13.329)